Recent researches have found that by equipping transmitters and receivers of wireless communication systems with large antenna arrays, large array gains and high data rate can be attained with low transmission power. Such communication scheme, known as Massive-Multiple Input Multiple Output (MIMO), is a promising technique for future wireless communication systems because of its high power efficiency. The benefits of Massive-MIMO may include power saving, high beam forming gain, low interference generation, and robustness. However, in order to achieve tremendous link gains via beamforming with Massive-MIMO, knowledge of accurate channel state information (CSI) could be required at the transmitter. The acquisition of precise CSI at the transmitter would be impractical in many cases such as frequency-division duplex (FDD) systems since additional feedbacks would be required for frequency-division duplex (FDD) systems. Also the feedback overheads could be overwhelming for Massive-MIMO because of the sheer quantities of transmitting and receiving antennas.
Apart from the beamforming techniques for which CSI is needed at the transmitter, Massive-MIMO is also advantageous to be incorporated into spatial-domain modulation schemes, in which CSI would not be needed at the transmitter side. The concept of spatial-domain modulation is to convey data by the spatial activation and deactivation patterns of transmitting antennas. The classic examples of Spatial-domain modulation may include spatial modulation (SM) and generalized space shift keying (GSSK).
For SM, information bit streams could first be segmented into two portions: the first portion could be conveyed by the index of the only one active antenna during each signaling interval, while the second portion could be signaled by the physically transmitted data symbol on that antenna.
An illustration of SM scheme is shown in FIG. 1. Assuming that the intended input bits in the left most column of FIG. 1 are 3-bits symbols. Based on the SM scheme, the first two bits could be conveyed by the index of the antenna in the second column. The third bit of each of the input bits could be signaled by the physically transmitted data symbol on that antenna. In this example, the first two bits of the input bits 000 are 00, so 00 is represented by the index 1 or the first antenna as illustrated in the second column of FIG. 1. The last bit of the input bits 000 is 0 which would be modulated by BPSK, and the therefore transmitting symbol of the last bit 0 would be −1 as illustrated in the third column. By following this mapping rule, each of the input bits could be transmitted on a particular antenna and signaled according to the currently configured modulation scheme.
On the other hand, in GSSK, the data is merely conveyed by indices set of active antennas. An exemplary mapping table for GSSK could be illustrated in FIG. 2. The first column of the mapping table of FIG. 2 could be any possible input bits. Assuming that there are five antennas, the activated antenna number in the second column indicates the indexes of any two activated antennas. The third column indicates transmitting signal vector of every input bits. For example, the input bits 001 would be transmitted by activated antenna 1 and activated antenna 3. The transmitting signal vector of the input bits 001 shows that only antenna 1 and antenna 3 transmit the signal. It would be apparent that the data rate of GSSK is dependent on the total number of active antennas. Accordingly, exploiting the resource on spatial domain is relatively more cost-effective than time and frequency domains. High data rate can be achieved with lower-order modulations. For instance, SM allows 3 bits to be transmitted using BPSK symbol with 4 transmitting antennas. Inter-antenna synchronization issues can be relented. Low hardware cost as the number of required RF chains is significantly reduced.
However, there are still several drawbacks in SM and GSSK. Under a fixed antenna array size, data rates for SM could be boosted by launching symbols of a higher-order IQ-modulation on the activated antenna. Increasing modulation order would be undesirable in noisy channel. For GSSK, the antenna array size and the number of activated antennas have to be increased in order to increase the bit size of a symbol. For instance, in order to represent 64-QAM (6 bits) with 8 antennas, at least 4 antennas should be turned on. To represent 256-QAM (8 bits) with 16 antenna, at least 3 antennas should be turned on. Moreover, maximum-likelihood (ML) detection has been suggested as the receiver's algorithm for both SM and GSSK, the computational complexity to perform ML would be unacceptable in practice despite its optimal performance. These aforementioned challenges could become issues of focus for those who are skilled in the art.